Experimental and theoretical studies show that two-dimensional (2D) materials have great potential applications in the fields of optoelectronics, semiconductors and spintronic devices. Based on the First Principles, the stability, band structure, electronic properties and optical properties of hydrogenated C3B, a new graphene like two-dimensional (2D) material, are studied in this paper. The results show that: firstly, with the increase of hydrogenation degree, the sp2 orbital hybridization in C3B structure gradually transits to a more stable sp3 mode, and the valence band energy near the Fermi level decreases; Secondly, adsorbed H atoms can regulate the bandgap of C3B. When the number of adsorbed H is even, C3B structure behaves as a semiconductor, and meanwhile the bandgap increases. When H atoms is odd, C3B is easy to show metallicity; Finally, the main absorption peak of the optical absorption spectrum decreases first and then increases with the increase of H concentration. The law of the secondary absorption peak is opposite to the main peak. When the ratio of hydrogenation is 50%, an obvious secondary absorption peak appears. This study confirms that hydrogenation is an effective way to regulate the electronic properties of materials, which can expand the application of 2D material C3B in optoelectronic devices.

Background: Acute type A aortic dissection (ATAAD) is a life-threatening medical condition requiring urgent surgical attention. It is estimated that 50% of ATAAD die within 24 hours of onset, with the mortality rate is increasing by 1-2% every additional hour without prompt intervention. A variety of ATAAD surgical repair techniques exist which has sparked controversy within the literature, with the main two strategies being proximal aortic replacement (PAR) and total arch replacement (TAR). Nevertheless, the question of which of these two strategies if the more optimal is still debatable. Aims: This commentary aims to discuss the recent study by Sa and colleagues which presents a pooled analysis of Kaplan-Meier-derived individual patient data from studies with follow-up comparing aggressive (TAR) and conservative (PAR) approaches to manage ATAAD patients. Methods: A comprehensive literature search was performed using multiple electronic databases including PubMed, Ovid, Google Scholar, EMBASE and Scopus in order to collate the relevant research evidence. Results: The more aggressive TAR approach for treating ATAAD seems to yield more favourable results including more optimal long-term survival as well as a lower need for reoperation. The frozen elephant trunk (FET) technique can be considered the mainstay TAR technique. Conclusion: It is valid to conclude that TAR with FET is the superior strategy for managing ATAAD patients.

The clinical and radiographic dilemma of fibrous dysplasia versus osteomyelitis is highlighted in this case of a 4-year-old girl with a bony hard swelling over in mandiblular body and ramus. Differences between histopathological appearance and radiographic presentation with the possible differential diagnosis were discussed and compared with previous studies.

Ecosystems wherein social and solitary predators share space, complex asymmetric intraguild interactions actively shape the group size of social carnivores. Intense intraguild predation has been known to result in reduced group size and low recruitment rates in subordinate social carnivores. In South and South-east Asia, Dhole, tiger and leopard form a widely distributed sympatric guild of large carnivores. In this paper we attempted to understand the pack size dynamics of a subordinate social predator, the dhole, by investigating factors underlying pack size variation at two neighbouring sites. We further evaluated our local-scale patterns of pack size variation at a larger scale by doing a distribution-wide assessment of pack size across dhole ranging countries. Across study sites, we found an inverse relationship between the local abundance of a top predator and group size of a subordinate predator while accounting for variability in resources and habitat heterogeneity. Tiger density was low (0.71/100 Km2) at the site having large dhole packs (16.8 ± 3.1) whereas, a relatively smaller average dhole pack size (6.4 ± 1.3) was observed in a higher tiger density (5.67/100 Km2) area. Our results on distribution-wide assessment are concurrent with local-scale results, showing a positive association between prey abundance and pack size and a negative association between tiger densities and dhole pack size. Our study takes us one step closer to trying to answer the age-old question of what drives the pack size of social predators in a multi-predator system. Linking behaviour to population dynamics and carnivore interactions is another highlight of the study. Often helpful while optimizing conservation triage and formulation of management implications like recovery and translocations.

The repeated occurrence of similar phenotypes in independent lineages (i.e., parallel evolution) in response to similar ecological conditions can provide compelling insights into the process of adaptive evolution. An intriguing question is to what extent repeated phenotypic changes are underlain by repeated changes at the genomic level and whether patterns of genomic divergence differ with the geographic context in which populations evolve. Here, we combine genomic, morphological and ecological datasets to investigate the genomic signatures of divergence across populations of threespine stickleback (Gasterosteus aculeatus) that adapted to contrasting trophic niches (benthic or limnetic) in either sympatry or allopatry. We found that genome-wide differentiation (FST) was an order of magnitude higher and substantially more repeatable for sympatric benthic and limnetic specialists compared to allopatric populations with similar levels of trophic divergence. We identified 55 genomic regions consistently differentiated between sympatric ecotypes that were also associated with benthic vs. limnetic niche across allopatric populations. These candidate regions were enriched on three chromosomes known to be involved in the benthic-limnetic divergence of threespine stickleback. Some candidate regions overlapped with QTL for body shape and trophic traits such as number of gill rakers, traits that strongly differ between benthic and limnetic ecotypes. In sum, our study shows that magnitude and repeatability of genomic signatures of trophic divergence in threespine stickleback highly depend on the geographical context. The identified candidate regions provide starting points to identify functionally important genes for the adaptation to benthic and limnetic trophic niches.

Introduced and invasive species make excellent natural experiments for investigating rapid evolution. Here, we describe the effects of genetic drift and rapid genetic adaptation in pink salmon (Oncorhynchus gorbuscha) that were accidentally introduced to the Great Lakes via a single introduction event 31-generations ago. Using whole-genome resequencing for 134 fish spanning five sample groups across the native and introduced range, we estimate that the progenitor population’s effective population size was 146,886 at the time of introduction, whereas the founding population’s effective population size was just 72—a 2040-fold decrease. As expected with a severe bottleneck, we show reductions in genome-wide measures of genetic diversity, specifically a 37.7% reduction in the number of SNPs and an 8.2% reduction in observed heterozygosity. Despite this decline in genetic diversity, we provide evidence for putative selection at 47 loci across multiple chromosomes in the introduced populations, including missense variants in genes associated with circadian rhythm, immunological response, and maturation, which match expected or known phenotypic changes in the Great Lakes. For one of these genes, we use a species-specific agent-based model to rule out genetic drift and conclude that a strong response to selection occurred in a period gene (per2) that plays a predominant role in determining an organism’s daily clock, matching large day length differences experienced by introduced salmon during important phenological periods. Together, these results inform how populations might evolve rapidly to new environments, even with a small pool of standing genetic variation.

Since the start of the COVID‐19 vaccinations, many dermatological manifestations have been described .There are certain safety-related events that, due to rarity, might be detected only during the mass vaccination programs. Here we report a potentially serious adverse effect of Covid vaccines that have been rarely reported before

The literature describes multiple approaches for the repair of stenosed branch pulmonary arteries. Regardless of the technique, restenosis is undesirably and notoriously common. We describe a case of severe left pulmonary artery stenosis repaired with a novel technique in consideration of factors leading to restenosis. The native main pulmonary artery was transected and turned down to create a direct anastomosis with the left pulmonary artery. The child had a normal sized main pulmonary artery with tricuspid atresia and pulmonary atresia with ductus arteriosus feeding the severely stenosed left pulmonary artery. Our novel technique resulted in hemodynamically gratifying results with a tension free tissue-tissue anastomosis with potential for growth.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are three-fold: firstly, we generate six training and evaluation datasets by employing advanced data augmentation and sampling methods. Secondly, we redesign the “zFormer” module, inspired by AlphaFold2’s Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Lastly, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing datasets, achieving Pearson’s correlation R = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged lDDT = 0.558 of AIchemy_LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Fungal infections cause serious problems in many aspects of human life; especially infections by fungal species represent problems in immunocompromised patients. Current antifungal antibiotics target various metabolic pathways, predominantly the cell wall or cellular membrane. However, numerous compounds are available to combat fungal infections, their efficacy is far from being satisfactory and some of them display substantial toxicity. The emerging resistance represents a serious issue as well; thus, there is a considerable need for new anti-fungal compounds with lower toxicity and higher effectiveness. One of the unique antifungal antibiotics is sordarin, the only known compound that acts on the fungal translational machinery per se. It has been shown that sordarin inhibits protein synthesis at the elongation step of the translational cycle, acting on eukaryotic elongation-factor-2. In this review, we are aiming to deliver a robust scientific platform promoting the development of antifungal compounds, especially focusing on molecular action of sordarin.

In Belgium, IWVA uses Managed Aquifer Recharge (MAR) to recharge the aquifer with treated wastewater generated from the communities to sustain the potable water supply on the Belgian coast. This MAR facility is faced with a challenge of reduced infiltration rates during the winter season when pond water temperatures near 4 °C. This study involves the identification of the predominant factor influencing the rate of infiltration through the pond bed. Several factors including pumping rates, natural recharge, tidal influences of the North Sea and pond-water temperature were identified as potential causes for variation of the recharge rate. Correlation statistics and linear regression analysis were used to determine the sensitivity of the infiltration rate to the aforementioned factors. Two groundwater flow models were developed in visual MODFLOW to simulate the water movement under the pond bed and to obtain the differences in flux to track the effects of variation of hydraulic conductivity during the two seasons. A 32 % reduction in vertical hydraulic gradient in the top portion of the aquifer was observed in winter causing the recharge rates to fluctuate. Results showed that water temperature caused a 30 % increase in hydraulic conductivity in summer as compared to winter and has the maximum impact on infiltration rate. Cyclic variations in water viscosity, occurring because of seasonal temperature changes, influence the saturated hydraulic conductivity of the pond bed. Results from the models confirm the impact on infiltration rate by temperature influenced hydraulic conductivity.

The recent uptick in the approval of ex vivo cell therapies highlight the relevance of Lentivirus (LV) as an enabling viral vector of modern medicine. As labile biologics, however, LVs pose critical challenges to industrial biomanufacturing. In particular, LV purification – currently reliant on filtration and anion-exchange or size-exclusion chromatography – suffers from long process times and low yield of transducing particles, which translate in high waiting time and cost to patients. Seeking to improve LV downstream processing, this study introduces peptides targeting the enveloped protein Vesicular stomatitis virus G (VSV-G) to serve as affinity ligands for the chromatographic purification of LV particles. An ensemble of candidate ligands was initially discovered by implementing a dual-fluorescence screening technology and a targeted in silico approach designed to identify sequences with high selectivity and tunable affinity. The selected peptides were conjugated on Poros resin and their LV binding-and-release performance was optimized by adjusting the flow rate, composition, and pH of the chromatographic buffers. Ligands GKEAAFAA and SRAFVGDADRD were selected for their high product yield (50-60% of viral genomes; 40-50% of HT1080 cell-transducing particles) upon elution in PIPES buffer with 0.65 M NaCl at pH 7.4. The peptide-based adsorbents also presented remarkable values of binding capacity (up to 3·10 9 TU per mL of resin at the residence time of 1 min) and clearance of host cell proteins (up to 220-fold reduction of HEK293 HCPs). Additionally, GKEAAFAA demonstrated high resistance to caustic cleaning-in-place (0.5 M NaOH, 30 min) with no observable loss in product yield and quality.

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (i) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict secondary structure, and (ii) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on two independent test datasets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the 82 protein targets of 2018 CASP13 experiment and achieved the best Q3 score of 83.74% and SOV score of 72.46%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

Brain mineralocorticoid receptors (MR) mediate effects of aldosterone in relation to salt homeostasis, and of glucocorticoid stress hormones corticosteroids in the context of stress adaptation. Brain stem MRs respond to aldosterone, while forebrain MRs mediate rapid and delayed MR-mediated glucocorticoids effects in conjunction with the glucocorticoid receptor. MR-mediated effects depend on gender, genetic variations and environmental influences. Disturbed MR activity by chronic stress or in certain (endocrine) diseases can cause deleterious effects on affective state, cognitive and behavioural function in susceptible individuals. High MR activation may have protective effects in healthy individuals, whereas dysregulated high MR activity during a stress response would require treatment with mineralocorticoid receptor antagonists (MRAs). Here, we discuss recent pharmacological and genetic developments, from the molecular underpinnings of MR signaling and function, to pharmacological interventions in the clinic. Improved understanding of MR dependent pathways will help to improve glucocorticoid therapy, unwanted side effects and psychiatric symptoms.

A document by David Dingli. Click on the document to view its contents.

Background: Few and small studies have described the management of immunomodulant/immunosuppressive therapies or phototherapy in atopic dermatitis (AD) patients during coronavirus disease 2019 (COVID-19) pandemic. Methods: A national registry, named DA-COVID-19 and involving 35 Italian dermatology units, was established in order to evaluate the impact of COVID-19 pandemic on the management of adult AD patients treated with systemic immunomodulant/immunosuppressive medications or phototherapy. Demographic and clinical data were obtained at different timepoints by teledermatology during COVID-19 pandemic, when regular visits were not allowed due to sanitary restrictions. Disease severity was assessed by both physician- and patient-reported assessment scores evaluating itch intensity, sleep disturbances, and AD severity. Results: A total of 1831 patients were included, with 1580/1831 (86.3%) continuing therapy during pandemic. Most patients were treated with dupilumab (86.1%, 1576/1831) that was interrupted in only 9.9% (156/1576) of cases, while systemic immunosuppressive compounds were more frequently withdrawn. Treatment interruption was due to decision of the patient, general practitioner or dermatologist in 39.9% (114/286), 5.6% (16/286), and 30.1% (86/286) of cases, respectively. Fear of increased susceptibility to SARS-CoV-2 infection (24.8%, 71/286) was one of the main causes of interruption. Sixteen patients (0.9%) resulted positive to SARS-CoV-2 infection, 3 of them (0.2%) were hospitalized but no cases of COVID-related death occurred. Conclusions: Most AD patients continued systemic treatments during COVID pandemic and lockdown period, without high impact on disease control, particularly dupilumab-treated patients.

The study was conducted in Lake Baringo and determined quantitative relationships between water level changes, water quality, and fishery production for informed lake basin management. Long-term (2008 to 2020) data on water level, water quality, and fisheries yields from Lake Baringo were analyzed using a combination of statistical methods. Linear and waveform regression analyses described patterns of lake level fluctuations over time while, Pearson’s correlation determined the concordance of lake level changes with water quality parameters, landings, and condition of fish species. PCA results grouped the study period into different years based on annual water quality variable levels. LOWESS analysis showed the decline of annual lake level amplitude over time with peak values in 1964 (8.6 m) and 2008 (9.4 m). The waveform regression significantly modeled lake level fluctuations as indexed by annual deviations from the long-term average (DLTM) and showed a 20-year oscillation between peak water levels in the lake. There were significant positive correlations of Water Level Fluctuations (WLFs) with water quality variables and water quality index (WQI) in Lake Baringo. Linear regression analyses showed a significant concordance (p < 0.05) between the annual fishery yield and the rising WLFs (r = 0.66). Overall, the results demonstrate that WLFs of Lake Baringo are a driver of fish species biomass and physico-chemical properties of the lake. We recommend the integration of fisheries yields, water quality assessment, and WLFs modeling at different temporal scales in the management of Afrotropical lake ecosystems

Aim: Threats faced by narrowly distributed endemic plant species in the face of the Earth’s sixth mass extinction and climate change exposure are especially severe for taxa on islands. We investigated the current and projected distribution and range changes of Cochemiea halei, an island endemic cactus. This taxon is of conservation concern, currently listed as vulnerable on the International Union for the Conservation of Nature Red List and as a species of special concern under Mexican federal law. The goals of this study are to 1). identify the correlations between climate variables and current suitable habitat for C. halei; 2). determine if the species is a serpentine endemic or has a facultative relationship with ultramafic soils; 3). predict range changes of the species based on climate change scenarios. Location: The island archipelago in Bahía Magdalena on the Pacific coast, Baja California Sur, Mexico. Methods: We used temperature and precipitation variables at 30 arcsecond resolution and soil type, employing multiple species distribution modeling methods, to identify important climate and soil conditions driving current habitat suitability. The best model of current suitability is used to predict possible effects of four climate change scenarios based on best case to worst case representative concentration pathways, with projected climate data from two general circulation models, over two time periods. Main conclusions: The occurrence of the species is found to be strongly correlated with ultramafic soils. The most important climate predictor for habitat suitability is annual temperature range. The species is predicted to undergo range contractions from 21% to 53%, depending on the severity and duration of exposure to climate change. The broader implications for a wide range of narrowly adapted, threatened and endemic plant species indicate an urgent need for threat assessment based on habitat suitability and climate change modeling.